In this paper, we propose a micro genetic algorithm with three forms of elitism for multiobjective optimization. We show how this relatively simple algorithm coupled with an external file and a diversity approach based on geographical distribution can generate efficiently the Pareto fronts of several difficult test functions (both constrained and unconstrained). A metric based on the average distance to the Pareto optimal set is used to compare our results against two evolutionary multiobjective optimization techniques recently proposed in the literature.